Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations25000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 MiB
Average record size in memory96.0 B

Variable types

Numeric12
Categorical5

Alerts

change is highly overall correlated with diabetes_medHigh correlation
diabetes_med is highly overall correlated with changeHigh correlation
glucose_test is highly imbalanced (77.1%)Imbalance
A1Ctest is highly imbalanced (50.5%)Imbalance
n_emergency is highly skewed (γ1 = 24.53015169)Skewed
age has 2532 (10.1%) zerosZeros
n_procedures has 11409 (45.6%) zerosZeros
n_outpatient has 20859 (83.4%) zerosZeros
n_inpatient has 16537 (66.1%) zerosZeros
n_emergency has 22272 (89.1%) zerosZeros
medical_specialty has 1409 (5.6%) zerosZeros
diag_1 has 7824 (31.3%) zerosZeros
diag_2 has 8134 (32.5%) zerosZeros
diag_3 has 7686 (30.7%) zerosZeros

Reproduction

Analysis started2024-08-28 15:57:40.630700
Analysis finished2024-08-28 15:58:29.325423
Duration48.69 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

age
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.34412
Minimum0
Maximum5
Zeros2532
Zeros (%)10.1%
Negative0
Negative (%)0.0%
Memory size97.8 KiB
2024-08-28T21:43:29.659270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3156332
Coefficient of variation (CV)0.56124822
Kurtosis-0.81550571
Mean2.34412
Median Absolute Deviation (MAD)1
Skewness-0.12617834
Sum58603
Variance1.7308907
MonotonicityNot monotonic
2024-08-28T21:43:29.989298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 6837
27.3%
2 5913
23.7%
4 4516
18.1%
1 4452
17.8%
0 2532
 
10.1%
5 750
 
3.0%
ValueCountFrequency (%)
0 2532
 
10.1%
1 4452
17.8%
2 5913
23.7%
3 6837
27.3%
4 4516
18.1%
5 750
 
3.0%
ValueCountFrequency (%)
5 750
 
3.0%
4 4516
18.1%
3 6837
27.3%
2 5913
23.7%
1 4452
17.8%
0 2532
 
10.1%

time_in_hospital
Real number (ℝ)

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.45332
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size195.4 KiB
2024-08-28T21:43:30.302113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile11
Maximum14
Range13
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.0014699
Coefficient of variation (CV)0.67398477
Kurtosis0.800598
Mean4.45332
Median Absolute Deviation (MAD)2
Skewness1.1089046
Sum111333
Variance9.0088213
MonotonicityNot monotonic
2024-08-28T21:43:30.621854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3 4311
17.2%
2 3986
15.9%
1 3480
13.9%
4 3467
13.9%
5 2542
10.2%
6 1895
7.6%
7 1467
 
5.9%
8 1104
 
4.4%
9 768
 
3.1%
10 588
 
2.4%
Other values (4) 1392
 
5.6%
ValueCountFrequency (%)
1 3480
13.9%
2 3986
15.9%
3 4311
17.2%
4 3467
13.9%
5 2542
10.2%
6 1895
7.6%
7 1467
 
5.9%
8 1104
 
4.4%
9 768
 
3.1%
10 588
 
2.4%
ValueCountFrequency (%)
14 281
 
1.1%
13 299
 
1.2%
12 354
 
1.4%
11 458
 
1.8%
10 588
 
2.4%
9 768
 
3.1%
8 1104
4.4%
7 1467
5.9%
6 1895
7.6%
5 2542
10.2%

n_lab_procedures
Real number (ℝ)

Distinct109
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.24076
Minimum1
Maximum113
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size195.4 KiB
2024-08-28T21:43:31.013577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q131
median44
Q357
95-th percentile74
Maximum113
Range112
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.81862
Coefficient of variation (CV)0.45833191
Kurtosis-0.29792189
Mean43.24076
Median Absolute Deviation (MAD)13
Skewness-0.23867244
Sum1081019
Variance392.77771
MonotonicityNot monotonic
2024-08-28T21:43:31.435232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 751
 
3.0%
43 638
 
2.6%
44 589
 
2.4%
45 572
 
2.3%
38 547
 
2.2%
39 538
 
2.2%
41 535
 
2.1%
49 521
 
2.1%
46 515
 
2.1%
40 513
 
2.1%
Other values (99) 19281
77.1%
ValueCountFrequency (%)
1 751
3.0%
2 268
 
1.1%
3 173
 
0.7%
4 102
 
0.4%
5 79
 
0.3%
6 68
 
0.3%
7 80
 
0.3%
8 90
 
0.4%
9 251
 
1.0%
10 219
 
0.9%
ValueCountFrequency (%)
113 1
 
< 0.1%
111 1
 
< 0.1%
109 1
 
< 0.1%
108 2
 
< 0.1%
106 2
 
< 0.1%
105 2
 
< 0.1%
103 1
 
< 0.1%
102 1
 
< 0.1%
101 5
< 0.1%
100 2
 
< 0.1%

n_procedures
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.35236
Minimum0
Maximum6
Zeros11409
Zeros (%)45.6%
Negative0
Negative (%)0.0%
Memory size195.4 KiB
2024-08-28T21:43:31.758602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7151793
Coefficient of variation (CV)1.268286
Kurtosis0.79556662
Mean1.35236
Median Absolute Deviation (MAD)1
Skewness1.3005718
Sum33809
Variance2.9418401
MonotonicityNot monotonic
2024-08-28T21:43:32.091339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 11409
45.6%
1 5098
20.4%
2 3064
 
12.3%
3 2395
 
9.6%
6 1227
 
4.9%
4 999
 
4.0%
5 808
 
3.2%
ValueCountFrequency (%)
0 11409
45.6%
1 5098
20.4%
2 3064
 
12.3%
3 2395
 
9.6%
4 999
 
4.0%
5 808
 
3.2%
6 1227
 
4.9%
ValueCountFrequency (%)
6 1227
 
4.9%
5 808
 
3.2%
4 999
 
4.0%
3 2395
 
9.6%
2 3064
 
12.3%
1 5098
20.4%
0 11409
45.6%

n_medications
Real number (ℝ)

Distinct70
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.2524
Minimum1
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size195.4 KiB
2024-08-28T21:43:32.477067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q111
median15
Q320
95-th percentile31
Maximum79
Range78
Interquartile range (IQR)9

Descriptive statistics

Standard deviation8.0605318
Coefficient of variation (CV)0.49595948
Kurtosis3.4765952
Mean16.2524
Median Absolute Deviation (MAD)5
Skewness1.316139
Sum406310
Variance64.972173
MonotonicityNot monotonic
2024-08-28T21:43:32.933389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 1509
 
6.0%
15 1469
 
5.9%
13 1459
 
5.8%
11 1396
 
5.6%
14 1396
 
5.6%
16 1379
 
5.5%
17 1271
 
5.1%
10 1268
 
5.1%
9 1157
 
4.6%
18 1140
 
4.6%
Other values (60) 11556
46.2%
ValueCountFrequency (%)
1 66
 
0.3%
2 83
 
0.3%
3 198
 
0.8%
4 275
 
1.1%
5 419
 
1.7%
6 631
2.5%
7 828
3.3%
8 1052
4.2%
9 1157
4.6%
10 1268
5.1%
ValueCountFrequency (%)
79 1
 
< 0.1%
75 1
 
< 0.1%
72 1
 
< 0.1%
69 2
 
< 0.1%
68 2
 
< 0.1%
65 2
 
< 0.1%
64 1
 
< 0.1%
63 5
< 0.1%
62 3
< 0.1%
61 4
< 0.1%

n_outpatient
Real number (ℝ)

ZEROS 

Distinct23
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3664
Minimum0
Maximum33
Zeros20859
Zeros (%)83.4%
Negative0
Negative (%)0.0%
Memory size195.4 KiB
2024-08-28T21:43:33.304963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum33
Range33
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.1954782
Coefficient of variation (CV)3.2627681
Kurtosis95.925322
Mean0.3664
Median Absolute Deviation (MAD)0
Skewness7.3026053
Sum9160
Variance1.4291682
MonotonicityNot monotonic
2024-08-28T21:43:33.650498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 20859
83.4%
1 2076
 
8.3%
2 913
 
3.7%
3 537
 
2.1%
4 269
 
1.1%
5 136
 
0.5%
6 74
 
0.3%
7 39
 
0.2%
8 18
 
0.1%
11 16
 
0.1%
Other values (13) 63
 
0.3%
ValueCountFrequency (%)
0 20859
83.4%
1 2076
 
8.3%
2 913
 
3.7%
3 537
 
2.1%
4 269
 
1.1%
5 136
 
0.5%
6 74
 
0.3%
7 39
 
0.2%
8 18
 
0.1%
9 13
 
0.1%
ValueCountFrequency (%)
33 1
 
< 0.1%
27 2
 
< 0.1%
23 1
 
< 0.1%
21 3
< 0.1%
20 2
 
< 0.1%
18 2
 
< 0.1%
16 2
 
< 0.1%
15 5
< 0.1%
14 7
< 0.1%
13 7
< 0.1%

n_inpatient
Real number (ℝ)

ZEROS 

Distinct16
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.61596
Minimum0
Maximum15
Zeros16537
Zeros (%)66.1%
Negative0
Negative (%)0.0%
Memory size195.4 KiB
2024-08-28T21:43:33.966831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum15
Range15
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1779511
Coefficient of variation (CV)1.9123825
Kurtosis16.454233
Mean0.61596
Median Absolute Deviation (MAD)0
Skewness3.2546341
Sum15399
Variance1.3875688
MonotonicityNot monotonic
2024-08-28T21:43:34.315120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 16537
66.1%
1 4926
 
19.7%
2 1909
 
7.6%
3 833
 
3.3%
4 358
 
1.4%
5 211
 
0.8%
6 104
 
0.4%
7 47
 
0.2%
8 26
 
0.1%
9 20
 
0.1%
Other values (6) 29
 
0.1%
ValueCountFrequency (%)
0 16537
66.1%
1 4926
 
19.7%
2 1909
 
7.6%
3 833
 
3.3%
4 358
 
1.4%
5 211
 
0.8%
6 104
 
0.4%
7 47
 
0.2%
8 26
 
0.1%
9 20
 
0.1%
ValueCountFrequency (%)
15 2
 
< 0.1%
14 2
 
< 0.1%
13 2
 
< 0.1%
12 3
 
< 0.1%
11 8
 
< 0.1%
10 12
 
< 0.1%
9 20
 
0.1%
8 26
 
0.1%
7 47
0.2%
6 104
0.4%

n_emergency
Real number (ℝ)

SKEWED  ZEROS 

Distinct21
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1866
Minimum0
Maximum64
Zeros22272
Zeros (%)89.1%
Negative0
Negative (%)0.0%
Memory size195.4 KiB
2024-08-28T21:43:34.647323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum64
Range64
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.88587348
Coefficient of variation (CV)4.7474463
Kurtosis1310.5933
Mean0.1866
Median Absolute Deviation (MAD)0
Skewness24.530152
Sum4665
Variance0.78477183
MonotonicityNot monotonic
2024-08-28T21:43:34.985914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 22272
89.1%
1 1842
 
7.4%
2 525
 
2.1%
3 167
 
0.7%
4 83
 
0.3%
5 40
 
0.2%
7 18
 
0.1%
6 18
 
0.1%
9 6
 
< 0.1%
8 6
 
< 0.1%
Other values (11) 23
 
0.1%
ValueCountFrequency (%)
0 22272
89.1%
1 1842
 
7.4%
2 525
 
2.1%
3 167
 
0.7%
4 83
 
0.3%
5 40
 
0.2%
6 18
 
0.1%
7 18
 
0.1%
8 6
 
< 0.1%
9 6
 
< 0.1%
ValueCountFrequency (%)
64 1
 
< 0.1%
37 1
 
< 0.1%
28 1
 
< 0.1%
21 1
 
< 0.1%
19 2
< 0.1%
18 3
< 0.1%
16 2
< 0.1%
13 1
 
< 0.1%
12 2
< 0.1%
11 3
< 0.1%

medical_specialty
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4588
Minimum0
Maximum6
Zeros1409
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size97.8 KiB
2024-08-28T21:43:35.301657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median4
Q34
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.4254486
Coefficient of variation (CV)0.41212231
Kurtosis0.3519299
Mean3.4588
Median Absolute Deviation (MAD)1
Skewness-0.84100624
Sum86470
Variance2.0319038
MonotonicityNot monotonic
2024-08-28T21:43:35.609393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 12382
49.5%
3 3565
 
14.3%
5 2664
 
10.7%
1 1885
 
7.5%
2 1882
 
7.5%
0 1409
 
5.6%
6 1213
 
4.9%
ValueCountFrequency (%)
0 1409
 
5.6%
1 1885
 
7.5%
2 1882
 
7.5%
3 3565
 
14.3%
4 12382
49.5%
5 2664
 
10.7%
6 1213
 
4.9%
ValueCountFrequency (%)
6 1213
 
4.9%
5 2664
 
10.7%
4 12382
49.5%
3 3565
 
14.3%
2 1882
 
7.5%
1 1885
 
7.5%
0 1409
 
5.6%

diag_1
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.29708
Minimum0
Maximum7
Zeros7824
Zeros (%)31.3%
Negative0
Negative (%)0.0%
Memory size97.8 KiB
2024-08-28T21:43:35.904387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q36
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.8277806
Coefficient of variation (CV)0.85766212
Kurtosis-1.7306832
Mean3.29708
Median Absolute Deviation (MAD)3
Skewness0.02586226
Sum82427
Variance7.9963433
MonotonicityNot monotonic
2024-08-28T21:43:36.232347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 7824
31.3%
6 6498
26.0%
7 3680
14.7%
2 2329
 
9.3%
1 1747
 
7.0%
3 1666
 
6.7%
5 1252
 
5.0%
4 4
 
< 0.1%
ValueCountFrequency (%)
0 7824
31.3%
1 1747
 
7.0%
2 2329
 
9.3%
3 1666
 
6.7%
4 4
 
< 0.1%
5 1252
 
5.0%
6 6498
26.0%
7 3680
14.7%
ValueCountFrequency (%)
7 3680
14.7%
6 6498
26.0%
5 1252
 
5.0%
4 4
 
< 0.1%
3 1666
 
6.7%
2 2329
 
9.3%
1 1747
 
7.0%
0 7824
31.3%

diag_2
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.33452
Minimum0
Maximum7
Zeros8134
Zeros (%)32.5%
Negative0
Negative (%)0.0%
Memory size97.8 KiB
2024-08-28T21:43:36.543640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q36
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.9135264
Coefficient of variation (CV)0.87374686
Kurtosis-1.8487802
Mean3.33452
Median Absolute Deviation (MAD)3
Skewness-0.042003843
Sum83363
Variance8.4886359
MonotonicityNot monotonic
2024-08-28T21:43:36.853686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
6 9056
36.2%
0 8134
32.5%
1 2906
 
11.6%
7 2872
 
11.5%
2 973
 
3.9%
3 591
 
2.4%
5 426
 
1.7%
4 42
 
0.2%
ValueCountFrequency (%)
0 8134
32.5%
1 2906
 
11.6%
2 973
 
3.9%
3 591
 
2.4%
4 42
 
0.2%
5 426
 
1.7%
6 9056
36.2%
7 2872
 
11.5%
ValueCountFrequency (%)
7 2872
 
11.5%
6 9056
36.2%
5 426
 
1.7%
4 42
 
0.2%
3 591
 
2.4%
2 973
 
3.9%
1 2906
 
11.6%
0 8134
32.5%

diag_3
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.14364
Minimum0
Maximum7
Zeros7686
Zeros (%)30.7%
Negative0
Negative (%)0.0%
Memory size97.8 KiB
2024-08-28T21:43:37.148412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q36
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.8372186
Coefficient of variation (CV)0.90252657
Kurtosis-1.8411175
Mean3.14364
Median Absolute Deviation (MAD)2
Skewness0.075262148
Sum78591
Variance8.0498095
MonotonicityNot monotonic
2024-08-28T21:43:37.465004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
6 9107
36.4%
0 7686
30.7%
1 4261
17.0%
7 1915
 
7.7%
2 916
 
3.7%
3 464
 
1.9%
5 455
 
1.8%
4 196
 
0.8%
ValueCountFrequency (%)
0 7686
30.7%
1 4261
17.0%
2 916
 
3.7%
3 464
 
1.9%
4 196
 
0.8%
5 455
 
1.8%
6 9107
36.4%
7 1915
 
7.7%
ValueCountFrequency (%)
7 1915
 
7.7%
6 9107
36.4%
5 455
 
1.8%
4 196
 
0.8%
3 464
 
1.9%
2 916
 
3.7%
1 4261
17.0%
0 7686
30.7%

glucose_test
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
1
23625 
2
 
689
0
 
686

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 23625
94.5%
2 689
 
2.8%
0 686
 
2.7%

Length

2024-08-28T21:43:37.808463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-28T21:43:38.122054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 23625
94.5%
2 689
 
2.8%
0 686
 
2.7%

Most occurring characters

ValueCountFrequency (%)
1 23625
94.5%
2 689
 
2.8%
0 686
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 25000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 23625
94.5%
2 689
 
2.8%
0 686
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 25000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 23625
94.5%
2 689
 
2.8%
0 686
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 25000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 23625
94.5%
2 689
 
2.8%
0 686
 
2.7%

A1Ctest
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
1
20938 
0
2827 
2
 
1235

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 20938
83.8%
0 2827
 
11.3%
2 1235
 
4.9%

Length

2024-08-28T21:43:38.438064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-28T21:43:38.721558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 20938
83.8%
0 2827
 
11.3%
2 1235
 
4.9%

Most occurring characters

ValueCountFrequency (%)
1 20938
83.8%
0 2827
 
11.3%
2 1235
 
4.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 25000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 20938
83.8%
0 2827
 
11.3%
2 1235
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 25000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 20938
83.8%
0 2827
 
11.3%
2 1235
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 25000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 20938
83.8%
0 2827
 
11.3%
2 1235
 
4.9%

change
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
13497 
1
11503 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 13497
54.0%
1 11503
46.0%

Length

2024-08-28T21:43:39.024307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-28T21:43:39.299679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 13497
54.0%
1 11503
46.0%

Most occurring characters

ValueCountFrequency (%)
0 13497
54.0%
1 11503
46.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 25000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 13497
54.0%
1 11503
46.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 25000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 13497
54.0%
1 11503
46.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 25000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 13497
54.0%
1 11503
46.0%

diabetes_med
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
1
19228 
0
5772 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 19228
76.9%
0 5772
 
23.1%

Length

2024-08-28T21:43:39.595304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-28T21:43:39.879938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 19228
76.9%
0 5772
 
23.1%

Most occurring characters

ValueCountFrequency (%)
1 19228
76.9%
0 5772
 
23.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 25000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 19228
76.9%
0 5772
 
23.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 25000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 19228
76.9%
0 5772
 
23.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 25000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 19228
76.9%
0 5772
 
23.1%

readmitted
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
13246 
1
11754 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 13246
53.0%
1 11754
47.0%

Length

2024-08-28T21:43:40.191082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-28T21:43:40.492041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 13246
53.0%
1 11754
47.0%

Most occurring characters

ValueCountFrequency (%)
0 13246
53.0%
1 11754
47.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 25000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 13246
53.0%
1 11754
47.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 25000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 13246
53.0%
1 11754
47.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 25000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 13246
53.0%
1 11754
47.0%

Interactions

2024-08-28T21:43:24.546707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:43.661365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:47.303181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:51.259692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:55.016509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:58.571071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:02.551437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:06.107255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:09.712285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:13.557253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:17.076548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:20.610410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:24.823953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:43.945005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:47.629215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:51.553963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:55.320649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:58.884791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:02.820504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:06.412821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:09.992307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:13.813437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:17.373833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:20.894304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:25.122068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:44.283850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:47.921328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:51.854039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:55.633902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:59.199893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:03.139731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:06.713286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:10.564154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:14.117673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:17.677318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:21.181770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:25.401855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:44.570869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:48.219841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:52.162297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:55.926039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:59.688021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:03.405954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:07.004530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:10.853426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:14.411578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:17.970713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:21.493632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:25.696790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:44.854015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:48.541674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:52.501861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:56.203981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:59.999913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:03.700031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:07.285769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:11.153078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:14.701973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:18.262779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:21.792741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:26.016673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:45.170187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:48.856714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:52.828318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:56.529436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:00.328077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:04.017570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:07.603321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:11.461680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:15.029958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:18.564983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:22.103012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:26.312842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:45.462008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:49.162929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:53.132795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:56.833453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:00.644379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:04.306917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:07.896100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:11.745254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:15.314615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:18.876496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:22.414418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:26.624279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:45.760213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:49.469741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:53.451274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:57.133342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:01.005850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:04.614059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:08.196222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:12.048081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:15.634455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:19.165703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:22.725999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:26.909833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:46.047612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:49.776429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:53.758431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:57.415203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:01.315052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:04.909724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:08.519988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:12.341053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:15.918369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:19.450909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:23.026376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:27.206325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:46.361433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:50.076380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:54.064039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:57.700040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:01.625534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:05.213724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:08.798193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:12.630291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:16.198254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:19.733255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:23.633385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:27.514040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:46.653769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:50.643174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:54.369734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:58.000775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:01.925839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:05.493542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:09.120583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:12.918117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:16.486641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:20.030158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:23.947165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:27.809442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:46.939315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:50.942885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:54.710178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:42:58.286854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:02.253825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:05.797378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:09.408580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:13.230817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:16.782380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:20.319242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-28T21:43:24.251597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-08-28T21:43:41.134844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A1Ctestagechangediabetes_meddiag_1diag_2diag_3glucose_testmedical_specialtyn_emergencyn_inpatientn_lab_proceduresn_medicationsn_outpatientn_proceduresreadmittedtime_in_hospital
A1Ctest1.0000.0700.1080.0810.0850.0430.0150.0520.0620.0000.0340.1880.0240.0160.0420.0230.053
age0.0701.0000.0550.035-0.024-0.022-0.0310.033-0.044-0.0380.0150.035-0.0350.020-0.0990.0420.101
change0.1080.0551.0000.5060.1100.0630.0350.0580.0840.0000.0190.0660.2420.0150.0190.0430.114
diabetes_med0.0810.0350.5061.0000.0880.0540.0310.0520.0340.0130.0310.0390.2070.0000.0210.0620.072
diag_10.085-0.0240.1100.0881.0000.1900.0950.0400.0690.0360.023-0.007-0.0210.020-0.2180.0560.026
diag_20.043-0.0220.0630.0540.1901.0000.0880.0330.0440.0560.0320.0570.0270.024-0.1000.0320.091
diag_30.015-0.0310.0350.0310.0950.0881.0000.0200.0220.0320.0300.0670.0160.016-0.0440.0390.077
glucose_test0.0520.0330.0580.0520.0400.0330.0201.0000.0820.0000.0210.2720.0300.0370.0540.0150.034
medical_specialty0.062-0.0440.0840.0340.0690.0440.0220.0821.000-0.0280.001-0.1130.1060.0700.0750.0560.028
n_emergency0.000-0.0380.0000.0130.0360.0560.0320.000-0.0281.0000.2200.0090.0450.176-0.0510.037-0.004
n_inpatient0.0340.0150.0190.0310.0230.0320.0300.0210.0010.2201.0000.0440.1010.168-0.0680.1910.094
n_lab_procedures0.1880.0350.0660.039-0.0070.0570.0670.272-0.1130.0090.0441.0000.259-0.0310.0150.0450.350
n_medications0.024-0.0350.2420.207-0.0210.0270.0160.0300.1060.0450.1010.2591.0000.0710.3370.0880.450
n_outpatient0.0160.0200.0150.0000.0200.0240.0160.0370.0700.1760.168-0.0310.0711.000-0.0300.059-0.018
n_procedures0.042-0.0990.0190.021-0.218-0.100-0.0440.0540.075-0.051-0.0680.0150.337-0.0301.0000.0470.177
readmitted0.0230.0420.0430.0620.0560.0320.0390.0150.0560.0370.1910.0450.0880.0590.0471.0000.053
time_in_hospital0.0530.1010.1140.0720.0260.0910.0770.0340.028-0.0040.0940.3500.450-0.0180.1770.0531.000

Missing values

2024-08-28T21:43:28.261611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-28T21:43:28.955640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

agetime_in_hospitaln_lab_proceduresn_proceduresn_medicationsn_outpatientn_inpatientn_emergencymedical_specialtydiag_1diag_2diag_3glucose_testA1Ctestchangediabetes_medreadmitted
03872118200407611010
13334213000566611010
21545018000400011111
33236012100406111111
4214207000360711010
50251010000466611001
61444221000436612110
72119616001506611011
84467313000326611001
93837118000277611110
agetime_in_hospitaln_lab_proceduresn_proceduresn_medicationsn_outpatientn_inpatientn_emergencymedical_specialtydiag_1diag_2diag_3glucose_testA1Ctestchangediabetes_medreadmitted
249901115110000651011000
249912445421000466312111
249921135518000000111000
24993229114000470011010
249942261411000400011110
2499541477130000406012001
249964266024000423610111
24997351206010466621001
249983261315000271611110
2499911037124000461011001